Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations130000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 MiB
Average record size in memory104.0 B

Variable types

Numeric7
Text2
DateTime2
Categorical1
Unsupported1

Alerts

Avg packet len is highly overall correlated with Data speed and 1 other fieldsHigh correlation
Data speed is highly overall correlated with Avg packet len and 1 other fieldsHigh correlation
Packet speed is highly overall correlated with Data speedHigh correlation
Type is highly overall correlated with Avg packet lenHigh correlation
Type is highly imbalanced (82.8%) Imbalance
Packet speed is highly skewed (γ1 = 57.68210892) Skewed
Data speed is highly skewed (γ1 = 52.04107752) Skewed
Avg source IP count is highly skewed (γ1 = 57.42175682) Skewed
Attack ID is uniformly distributed Uniform
Attack ID has unique values Unique
Duration is an unsupported type, check if it needs cleaning or further analysis Unsupported
Port number has 66483 (51.1%) zeros Zeros
Avg packet len has 4178 (3.2%) zeros Zeros

Reproduction

Analysis started2025-03-09 13:34:17.417191
Analysis finished2025-03-09 13:34:23.138410
Duration5.72 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

Attack ID
Real number (ℝ)

Uniform  Unique 

Distinct130000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637941.5
Minimum572942
Maximum702941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:23.424733image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum572942
5-th percentile579441.95
Q1605441.75
median637941.5
Q3670441.25
95-th percentile696441.05
Maximum702941
Range129999
Interquartile range (IQR)64999.5

Descriptive statistics

Standard deviation37527.912
Coefficient of variation (CV)0.058826572
Kurtosis-1.2
Mean637941.5
Median Absolute Deviation (MAD)32500
Skewness0
Sum8.2932395 × 1010
Variance1.4083442 × 109
MonotonicityStrictly increasing
2025-03-09T13:34:23.519087image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702941 1
 
< 0.1%
572942 1
 
< 0.1%
572943 1
 
< 0.1%
572944 1
 
< 0.1%
572945 1
 
< 0.1%
572946 1
 
< 0.1%
572947 1
 
< 0.1%
572948 1
 
< 0.1%
572949 1
 
< 0.1%
572950 1
 
< 0.1%
Other values (129990) 129990
> 99.9%
ValueCountFrequency (%)
572942 1
< 0.1%
572943 1
< 0.1%
572944 1
< 0.1%
572945 1
< 0.1%
572946 1
< 0.1%
572947 1
< 0.1%
572948 1
< 0.1%
572949 1
< 0.1%
572950 1
< 0.1%
572951 1
< 0.1%
ValueCountFrequency (%)
702941 1
< 0.1%
702940 1
< 0.1%
702939 1
< 0.1%
702938 1
< 0.1%
702937 1
< 0.1%
702936 1
< 0.1%
702935 1
< 0.1%
702934 1
< 0.1%
702933 1
< 0.1%
702932 1
< 0.1%
Distinct5860
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:23.637754image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9609308
Min length7

Characters and Unicode

Total characters1034921
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3677 ?
Unique (%)2.8%

Sample

1st rowIP_43014
2nd rowIP_59468
3rd rowIP_19543
4th rowIP_21200
5th rowIP_3403
ValueCountFrequency (%)
ip_59468 18477
 
14.2%
ip_60976 13732
 
10.6%
ip_50126 4433
 
3.4%
ip_3403 4359
 
3.4%
ip_62577 4158
 
3.2%
ip_58857 4034
 
3.1%
ip_54248 3641
 
2.8%
ip_56816 3083
 
2.4%
ip_64024 2312
 
1.8%
ip_60814 2287
 
1.8%
Other values (5850) 69484
53.4%
2025-03-09T13:34:23.834448image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 130000
12.6%
P 130000
12.6%
_ 130000
12.6%
6 127153
12.3%
4 81144
7.8%
5 81039
7.8%
9 64509
6.2%
8 56936
5.5%
0 56711
5.5%
2 51019
 
4.9%
Other values (3) 126410
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1034921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 130000
12.6%
P 130000
12.6%
_ 130000
12.6%
6 127153
12.3%
4 81144
7.8%
5 81039
7.8%
9 64509
6.2%
8 56936
5.5%
0 56711
5.5%
2 51019
 
4.9%
Other values (3) 126410
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1034921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 130000
12.6%
P 130000
12.6%
_ 130000
12.6%
6 127153
12.3%
4 81144
7.8%
5 81039
7.8%
9 64509
6.2%
8 56936
5.5%
0 56711
5.5%
2 51019
 
4.9%
Other values (3) 126410
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1034921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 130000
12.6%
P 130000
12.6%
_ 130000
12.6%
6 127153
12.3%
4 81144
7.8%
5 81039
7.8%
9 64509
6.2%
8 56936
5.5%
0 56711
5.5%
2 51019
 
4.9%
Other values (3) 126410
12.2%

Port number
Real number (ℝ)

Zeros 

Distinct19768
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15808.226
Minimum0
Maximum65535
Zeros66483
Zeros (%)51.1%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:23.927430image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340163.5
95-th percentile61719.1
Maximum65535
Range65535
Interquartile range (IQR)40163.5

Descriptive statistics

Standard deviation24248.41
Coefficient of variation (CV)1.5339109
Kurtosis-0.74917579
Mean15808.226
Median Absolute Deviation (MAD)0
Skewness1.0473936
Sum2.0550694 × 109
Variance5.8798541 × 108
MonotonicityNot monotonic
2025-03-09T13:34:24.014022image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66483
51.1%
443 16589
 
12.8%
7777 4633
 
3.6%
22552 504
 
0.4%
11198 497
 
0.4%
40072 399
 
0.3%
51820 375
 
0.3%
39327 256
 
0.2%
3400 207
 
0.2%
41821 205
 
0.2%
Other values (19758) 39852
30.7%
ValueCountFrequency (%)
0 66483
51.1%
1 3
 
< 0.1%
21 1
 
< 0.1%
22 203
 
0.2%
23 5
 
< 0.1%
25 17
 
< 0.1%
40 7
 
< 0.1%
53 108
 
0.1%
60 20
 
< 0.1%
80 169
 
0.1%
ValueCountFrequency (%)
65535 4
< 0.1%
65534 2
 
< 0.1%
65533 1
 
< 0.1%
65532 2
 
< 0.1%
65531 1
 
< 0.1%
65530 2
 
< 0.1%
65529 3
 
< 0.1%
65528 1
 
< 0.1%
65525 2
 
< 0.1%
65524 8
< 0.1%
Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:24.081914image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length126
Median length19
Mean length18.602585
Min length3

Characters and Unicode

Total characters2418336
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.1%

Sample

1st rowHigh volume traffic
2nd rowHigh volume traffic
3rd rowHigh volume traffic
4th rowICMP
5th rowHigh volume traffic
ValueCountFrequency (%)
traffic 125793
32.8%
high 125527
32.8%
volume 125527
32.8%
icmp 3548
 
0.9%
syn 577
 
0.2%
attack 577
 
0.2%
dns 489
 
0.1%
suspicious 266
 
0.1%
ntp 234
 
0.1%
wsd 115
 
< 0.1%
Other values (14) 574
 
0.1%
2025-03-09T13:34:24.249382image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
253227
 
10.5%
i 252042
 
10.4%
f 251670
 
10.4%
t 127133
 
5.3%
c 126858
 
5.2%
a 126606
 
5.2%
u 126059
 
5.2%
r 125965
 
5.2%
e 125957
 
5.2%
o 125922
 
5.2%
Other values (30) 776897
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2418336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
253227
 
10.5%
i 252042
 
10.4%
f 251670
 
10.4%
t 127133
 
5.3%
c 126858
 
5.2%
a 126606
 
5.2%
u 126059
 
5.2%
r 125965
 
5.2%
e 125957
 
5.2%
o 125922
 
5.2%
Other values (30) 776897
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2418336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
253227
 
10.5%
i 252042
 
10.4%
f 251670
 
10.4%
t 127133
 
5.3%
c 126858
 
5.2%
a 126606
 
5.2%
u 126059
 
5.2%
r 125965
 
5.2%
e 125957
 
5.2%
o 125922
 
5.2%
Other values (30) 776897
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2418336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
253227
 
10.5%
i 252042
 
10.4%
f 251670
 
10.4%
t 127133
 
5.3%
c 126858
 
5.2%
a 126606
 
5.2%
u 126059
 
5.2%
r 125965
 
5.2%
e 125957
 
5.2%
o 125922
 
5.2%
Other values (30) 776897
32.1%

Detect count
Real number (ℝ)

Distinct846
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4880692
Minimum1
Maximum5679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:24.330755image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile11
Maximum5679
Range5678
Interquartile range (IQR)1

Descriptive statistics

Standard deviation88.176086
Coefficient of variation (CV)9.2933645
Kurtosis554.32533
Mean9.4880692
Median Absolute Deviation (MAD)0
Skewness19.731088
Sum1233449
Variance7775.0221
MonotonicityNot monotonic
2025-03-09T13:34:24.415681image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 77332
59.5%
2 20470
 
15.7%
3 9033
 
6.9%
4 5167
 
4.0%
5 3212
 
2.5%
6 2361
 
1.8%
7 1944
 
1.5%
8 1568
 
1.2%
9 1262
 
1.0%
10 901
 
0.7%
Other values (836) 6750
 
5.2%
ValueCountFrequency (%)
1 77332
59.5%
2 20470
 
15.7%
3 9033
 
6.9%
4 5167
 
4.0%
5 3212
 
2.5%
6 2361
 
1.8%
7 1944
 
1.5%
8 1568
 
1.2%
9 1262
 
1.0%
10 901
 
0.7%
ValueCountFrequency (%)
5679 1
< 0.1%
4645 1
< 0.1%
4549 1
< 0.1%
3699 1
< 0.1%
2917 1
< 0.1%
2862 1
< 0.1%
2756 1
< 0.1%
2747 1
< 0.1%
2744 1
< 0.1%
2679 1
< 0.1%

Packet speed
Real number (ℝ)

High correlation  Skewed 

Distinct16108
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67797.89
Minimum5500
Maximum7475824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:24.501759image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum5500
5-th percentile51100
Q156000
median64000
Q373100
95-th percentile89100
Maximum7475824
Range7470324
Interquartile range (IQR)17100

Descriptive statistics

Standard deviation62404.222
Coefficient of variation (CV)0.9204449
Kurtosis4378.5196
Mean67797.89
Median Absolute Deviation (MAD)8400
Skewness57.682109
Sum8.8137257 × 109
Variance3.8942869 × 109
MonotonicityNot monotonic
2025-03-09T13:34:24.594876image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51700 632
 
0.5%
50400 626
 
0.5%
50800 616
 
0.5%
51200 604
 
0.5%
55000 577
 
0.4%
52000 573
 
0.4%
51000 562
 
0.4%
51600 559
 
0.4%
50600 547
 
0.4%
52800 545
 
0.4%
Other values (16098) 124159
95.5%
ValueCountFrequency (%)
5500 1
< 0.1%
7900 1
< 0.1%
8900 1
< 0.1%
9300 1
< 0.1%
10100 1
< 0.1%
10400 1
< 0.1%
12200 1
< 0.1%
12500 1
< 0.1%
13000 1
< 0.1%
13500 1
< 0.1%
ValueCountFrequency (%)
7475824 1
< 0.1%
5828739 1
< 0.1%
5709796 1
< 0.1%
4847726 1
< 0.1%
4326725 1
< 0.1%
4111053 1
< 0.1%
3991276 1
< 0.1%
3856093 1
< 0.1%
3840000 1
< 0.1%
3793881 1
< 0.1%

Data speed
Real number (ℝ)

High correlation  Skewed 

Distinct415
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.554477
Minimum0
Maximum6702
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:24.684605image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q165
median73
Q386
95-th percentile111
Maximum6702
Range6702
Interquartile range (IQR)21

Descriptive statistics

Standard deviation56.97917
Coefficient of variation (CV)0.75414684
Kurtosis3953.3847
Mean75.554477
Median Absolute Deviation (MAD)10
Skewness52.041078
Sum9822082
Variance3246.6259
MonotonicityNot monotonic
2025-03-09T13:34:24.775306image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 4358
 
3.4%
71 4255
 
3.3%
68 4204
 
3.2%
72 4186
 
3.2%
73 4078
 
3.1%
67 4064
 
3.1%
69 4063
 
3.1%
66 3927
 
3.0%
65 3489
 
2.7%
74 3480
 
2.7%
Other values (405) 89896
69.2%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 18
 
< 0.1%
2 52
 
< 0.1%
3 1363
1.0%
4 2237
1.7%
5 363
 
0.3%
6 130
 
0.1%
7 110
 
0.1%
8 190
 
0.1%
9 168
 
0.1%
ValueCountFrequency (%)
6702 1
< 0.1%
4779 1
< 0.1%
4667 1
< 0.1%
4449 1
< 0.1%
4413 1
< 0.1%
3874 1
< 0.1%
3645 1
< 0.1%
3644 1
< 0.1%
3623 1
< 0.1%
3600 1
< 0.1%

Avg packet len
Real number (ℝ)

High correlation  Zeros 

Distinct1299
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1176.7021
Minimum0
Maximum1518
Zeros4178
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:24.864570image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile546
Q11061
median1283
Q31356
95-th percentile1498
Maximum1518
Range1518
Interquartile range (IQR)295

Descriptive statistics

Standard deviation318.14459
Coefficient of variation (CV)0.27036968
Kurtosis5.0378594
Mean1176.7021
Median Absolute Deviation (MAD)169
Skewness-2.1069632
Sum1.5297128 × 108
Variance101215.98
MonotonicityNot monotonic
2025-03-09T13:34:24.954902image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1284 7644
 
5.9%
1498 6144
 
4.7%
1285 5432
 
4.2%
0 4178
 
3.2%
1068 3431
 
2.6%
874 3256
 
2.5%
1286 2837
 
2.2%
1283 2461
 
1.9%
1358 1940
 
1.5%
1278 1823
 
1.4%
Other values (1289) 90854
69.9%
ValueCountFrequency (%)
0 4178
3.2%
20 1
 
< 0.1%
46 24
 
< 0.1%
47 12
 
< 0.1%
51 3
 
< 0.1%
55 1
 
< 0.1%
57 11
 
< 0.1%
58 23
 
< 0.1%
59 9
 
< 0.1%
60 3
 
< 0.1%
ValueCountFrequency (%)
1518 270
0.2%
1517 43
 
< 0.1%
1516 20
 
< 0.1%
1515 15
 
< 0.1%
1514 29
 
< 0.1%
1513 16
 
< 0.1%
1512 42
 
< 0.1%
1511 7
 
< 0.1%
1510 85
 
0.1%
1509 10
 
< 0.1%

Avg source IP count
Real number (ℝ)

Skewed 

Distinct368
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7259846
Minimum1
Maximum18602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1015.8 KiB
2025-03-09T13:34:25.043186image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile9
Maximum18602
Range18601
Interquartile range (IQR)1

Descriptive statistics

Standard deviation197.96204
Coefficient of variation (CV)22.686499
Kurtosis3872.3504
Mean8.7259846
Median Absolute Deviation (MAD)0
Skewness57.421757
Sum1134378
Variance39188.968
MonotonicityNot monotonic
2025-03-09T13:34:25.133898image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 82605
63.5%
2 17528
 
13.5%
3 10354
 
8.0%
4 6431
 
4.9%
5 3051
 
2.3%
6 1541
 
1.2%
7 1054
 
0.8%
8 799
 
0.6%
9 591
 
0.5%
10 420
 
0.3%
Other values (358) 5626
 
4.3%
ValueCountFrequency (%)
1 82605
63.5%
2 17528
 
13.5%
3 10354
 
8.0%
4 6431
 
4.9%
5 3051
 
2.3%
6 1541
 
1.2%
7 1054
 
0.8%
8 799
 
0.6%
9 591
 
0.5%
10 420
 
0.3%
ValueCountFrequency (%)
18602 1
< 0.1%
17477 1
< 0.1%
17217 1
< 0.1%
16094 1
< 0.1%
14672 1
< 0.1%
14590 1
< 0.1%
14190 1
< 0.1%
13101 1
< 0.1%
12965 1
< 0.1%
12071 1
< 0.1%
Distinct128541
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1015.8 KiB
Minimum2023-04-27 12:38:37
Maximum2023-08-25 18:05:56
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T13:34:25.225274image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:25.316838image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct128494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size1015.8 KiB
Minimum2023-04-27 12:38:38
Maximum2023-08-25 18:05:57
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T13:34:25.401573image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:25.491354image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1015.8 KiB
Normal traffic
124880 
Suspicious traffic
 
3673
DDoS attack
 
1447

Length

Max length18
Median length14
Mean length14.079623
Min length11

Characters and Unicode

Total characters1830351
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal traffic
2nd rowNormal traffic
3rd rowNormal traffic
4th rowSuspicious traffic
5th rowNormal traffic

Common Values

ValueCountFrequency (%)
Normal traffic 124880
96.1%
Suspicious traffic 3673
 
2.8%
DDoS attack 1447
 
1.1%

Length

2025-03-09T13:34:25.576473image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T13:34:25.646225image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
traffic 128553
49.4%
normal 124880
48.0%
suspicious 3673
 
1.4%
ddos 1447
 
0.6%
attack 1447
 
0.6%

Most occurring characters

ValueCountFrequency (%)
f 257106
14.0%
a 256327
14.0%
r 253433
13.8%
i 135899
7.4%
c 133673
7.3%
t 131447
7.2%
130000
7.1%
o 130000
7.1%
N 124880
6.8%
l 124880
6.8%
Other values (7) 152706
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1830351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 257106
14.0%
a 256327
14.0%
r 253433
13.8%
i 135899
7.4%
c 133673
7.3%
t 131447
7.2%
130000
7.1%
o 130000
7.1%
N 124880
6.8%
l 124880
6.8%
Other values (7) 152706
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1830351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 257106
14.0%
a 256327
14.0%
r 253433
13.8%
i 135899
7.4%
c 133673
7.3%
t 131447
7.2%
130000
7.1%
o 130000
7.1%
N 124880
6.8%
l 124880
6.8%
Other values (7) 152706
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1830351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 257106
14.0%
a 256327
14.0%
r 253433
13.8%
i 135899
7.4%
c 133673
7.3%
t 131447
7.2%
130000
7.1%
o 130000
7.1%
N 124880
6.8%
l 124880
6.8%
Other values (7) 152706
8.3%

Duration
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1015.8 KiB

Interactions

2025-03-09T13:34:22.267561image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.330383image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.826456image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.313161image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.787918image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.278179image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.770516image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.337188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.398538image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.894875image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.381367image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.856455image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.349179image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.842374image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.406770image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.469115image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.962921image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.447042image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.924796image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.416119image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.912028image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.474374image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.539520image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.028275image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.509938image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.991788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.483200image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.978465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.547435image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.613250image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.101460image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.579682image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.063154image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.556282image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.049735image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.622037image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.683449image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.171641image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.648653image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.133891image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.627845image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.124838image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.694572image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:19.754179image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.242972image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:20.717526image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.204188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:21.698398image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T13:34:22.196745image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-03-09T13:34:25.695165image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Attack IDAvg packet lenAvg source IP countData speedDetect countPacket speedPort numberType
Attack ID1.0000.0090.0540.0140.033-0.010-0.0190.068
Avg packet len0.0091.000-0.4840.621-0.093-0.1450.0380.621
Avg source IP count0.054-0.4841.000-0.2960.0320.032-0.0220.171
Data speed0.0140.621-0.2961.0000.0120.5980.2530.141
Detect count0.033-0.0930.0320.0121.0000.187-0.0960.107
Packet speed-0.010-0.1450.0320.5980.1871.0000.1960.155
Port number-0.0190.038-0.0220.253-0.0960.1961.0000.112
Type0.0680.6210.1710.1410.1070.1550.1121.000

Missing values

2025-03-09T13:34:22.798569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T13:34:22.970350image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Attack IDVictim IPPort numberAttack codeDetect countPacket speedData speedAvg packet lenAvg source IP countStart timeEnd timeTypeDuration
0572942IP_4301452923High volume traffic18210099127322023-04-27 12:38:372023-04-27 12:38:38Normal traffic0 days 00:00:01
1572943IP_59468443High volume traffic16120061105132023-04-27 12:38:532023-04-27 12:38:54Normal traffic0 days 00:00:01
2572944IP_1954358702High volume traffic26130074129342023-04-27 12:39:532023-04-27 12:40:50Normal traffic0 days 00:00:57
3572945IP_212000ICMP15020030522023-04-27 12:40:152023-04-27 12:40:16Suspicious traffic0 days 00:00:01
4572946IP_340321776High volume traffic17100084126272023-04-27 12:40:242023-04-27 12:40:25Normal traffic0 days 00:00:01
5572947IP_625770High volume traffic7778866687412023-04-27 12:40:552023-04-27 12:41:04Normal traffic0 days 00:00:09
6572948IP_568160High volume traffic164200814112023-04-27 12:41:352023-04-27 12:41:36Normal traffic0 days 00:00:01
7572949IP_627910High volume traffic16250089149812023-04-27 12:43:332023-04-27 12:43:34Normal traffic0 days 00:00:01
8572950IP_5946844058High volume traffic28070081105542023-04-27 12:43:452023-04-27 12:43:50Normal traffic0 days 00:00:05
9572951IP_1954363477High volume traffic26710081127922023-04-27 12:43:552023-04-27 12:43:58Normal traffic0 days 00:00:03
Attack IDVictim IPPort numberAttack codeDetect countPacket speedData speedAvg packet lenAvg source IP countStart timeEnd timeTypeDuration
129990702932IP_542480High volume traffic15530079149812023-08-25 17:57:452023-08-25 17:57:46Normal traffic0 days 00:00:01
129991702933IP_428190High volume traffic15180048112023-08-25 17:59:362023-08-25 17:59:37Normal traffic0 days 00:00:01
129992702934IP_605320High volume traffic45577579148012023-08-25 18:01:072023-08-25 18:03:17Normal traffic0 days 00:02:10
129993702935IP_594680High volume traffic26745068106622023-08-25 18:01:212023-08-25 18:03:18Normal traffic0 days 00:01:57
129994702936IP_674030High volume traffic15010067140112023-08-25 18:01:312023-08-25 18:01:32Normal traffic0 days 00:00:01
129995702937IP_674990High volume traffic1704003857612023-08-25 18:02:512023-08-25 18:02:52Normal traffic0 days 00:00:01
129996702938IP_47910443High volume traffic2219050258574172023-08-25 18:03:062023-08-25 18:03:09Normal traffic0 days 00:00:03
129997702939IP_671400High volume traffic16370091149812023-08-25 18:04:212023-08-25 18:04:22Normal traffic0 days 00:00:01
129998702940IP_67500443High volume traffic3583003359412023-08-25 18:04:422023-08-25 18:05:46Normal traffic0 days 00:01:04
129999702941IP_674960High volume traffic15080060125612023-08-25 18:05:562023-08-25 18:05:57Normal traffic0 days 00:00:01